Top Tweets for #MLlib19
Nice - Overviews and slides from all the Machine Learning and Libraries talks at the Library of Congress are now up. #MLLib19
Overviews: https://t.co/98U2fnjkRd
Slides: https://t.co/vCB3yWk1uy
Conversations from #mllib19, #DH2020 & #DigitalDesignWeekend #LDF19 @V_and_A show that institutions make great efforts to keep up with #DigitalInnovation #AI #MachineLeaning #VR #AR #3D #Robotics. It's crucial to keep this dialogue global & to include it in policy-making 3/5
Reminder from @glagolista about bringing out the human, artistic content of digital images. Contrast to the huge, "common" sets of data (ie stored by Google) which are not necessarily trained for "synthesized" works (ie compressed images of a vacation) #MLLib19 #latentscapes
Let’s Move in Libraries Advisory Board member (and library director) Nicole Miller speaking NOW at #MLlib19
She got her board to find fitness classes at the library! Change isn’t possible unless you speak up!
Nicole Miller was able to persuade the library board to fund her group fitness certification to lead classes at no charge to participants #FunAndFitness #mnlib19
Now on Heather Yager from MIT, whose talk has changed due to the recent kerfuffle there. Nice to see another paper dealing with ethics, diversity and all that matters. Can't say that these issues are being ignored. #mllib19
Now on @pleonard from Yale, demoing https://t.co/leMHa5Xdtq showing 31k images from the YCBA Nice demo using pretrained convolutional NN. Uses WebGl for front end, kmeans clustering. #mllib19
Dominique Luster reporting back on one of the design thinking exercises. This group have come back partly be describing the LEAN business canvas model. #mllib19
Properly documented characteristics of a successful machine learning project. #MLLib19
Now @rdikow Mike Trizna and Corey DiPietro speaking about ML at the @smithsonian (actively looking for postdocs at the moment). #mllib19 Examples of the work they are doing - NN looking at ferns (5k species), "Because of Her Story" project https://t.co/fPoRCp3uX1
Reminder from @glagolista about bringing out the human, artistic content of digital images. Contrast to the huge, "common" sets of data (ie stored by Google) which are not necessarily trained for "synthesized" works (ie compressed images of a vacation) #MLLib19 #latentscapes
Nick Adams from Goodly Labs on #MachineLearning - "we need tools that are totally transparent on the providence of the data ... the goal is explainable and interpretable models" #MLLib19
What's real vs fake? @j_a_tucker at @monkeycageblog might have a solution to combating fake news. Check out this interview with Nick Adams of Goodly Labs about a tool called Public Editor that helps ordinary citizens vet the quality of online news articles https://t.co/89UfgKPREc
Important commentary by @hmaring on developing #MachineLearning in institutions vs industry - the fear is if these institutions don't act now they are going to be forced to be using solutions that come from the vendors #MLLib19 #BlackBoxSolutions
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